#!/usr/bin/env python3

import torch
from diffusers import FluxPipeline, DPMSolverMultistepScheduler
from BeamDiffusionModel.models.diffusionModel.configs.config_loader import CONFIG
from functools import partial
from BeamDiffusionModel.models.diffusionModel.Latents_Singleton import Latents

class Flux:
    def __init__(self):
        self.device = "cuda" if CONFIG.get("flux", {}).get("use_cuda", True) and torch.cuda.is_available() else "cpu"
        self.torch_dtype = torch.bfloat16 if CONFIG.get("flux", {}).get("precision") == "bfloat16" else torch.float16

        print(f"Loading model: {CONFIG['flux']['id']} on {self.device}")

        self.pipe = FluxPipeline.from_pretrained(CONFIG["flux"]["id"], torch_dtype=torch.bfloat16)
        self.pipe.enable_sequential_cpu_offload()
        self.pipe.vae.enable_slicing()
        self.pipe.vae.enable_tiling()
        self.pipe.tokenizer.truncation_side = 'left'

        print("Model loaded successfully!")


    def capture_latents(self, latents_store: Latents, pipe, step, timestep, callback_kwargs):
        latents = callback_kwargs["latents"]
        latents_store.add_latents(latents)
        return callback_kwargs

    def generate_image(self, prompt: str, latent=None, generator=None):
        latents = Latents()
        callback = partial(self.capture_latents, latents)
        img = self.pipe(prompt, latents=latent, callback_on_step_end=callback,
                             generator=generator, callback_on_step_end_tensor_inputs=["latents"],
                            height=768,
                            width=768,
                            guidance_scale=3.5,
                            max_sequence_length=512,
                             num_inference_steps=CONFIG["flux"]["diffusion_settings"]["steps"]).images[0]

        return img, latents.dump_and_clear()